TiCoder: Interactive Code Generation via Test-Driven User-IntentFormalization (Microsoft)
Test-driven user-intent formalization (or test-driven user-intent discovery): to create an interactive framework to (a) refine and formalize the user intent through generated tests, and (b) generate code that is consistent with such tests.
Test-driven user-intent formalization (or test-driven user-intent discovery): to create an interactive framework to (a) refine and formalize the user intent through generated tests, and (b) generate code that is consistent with such tests.
Time-Series Anomaly Detection with Implicit Neural Representation
Some ML4SE tasks are related to time series (anomaly detection in logs, forecasting in resource management, etc.). A novel method called Implicit Neural Representation-based Anomaly Detection (INRAD) is proposed. It uses error-based anomaly detection strategy. Using MLP, it learns to predict the value of a time series by a timestamp. The timestamp is the only input.
Some ML4SE tasks are related to time series (anomaly detection in logs, forecasting in resource management, etc.). A novel method called Implicit Neural Representation-based Anomaly Detection (INRAD) is proposed. It uses error-based anomaly detection strategy. Using MLP, it learns to predict the value of a time series by a timestamp. The timestamp is the only input.
HyperTime: Implicit Neural Representation for Time Series
This architecture leverages INRs to learn a compressed latent representation of an entire time series dataset. The output of the HyperNet is a one-dimensional 7500-values embedding that contains the network weights of an INR (HypoNet) which encodes the time series data from the input.
This architecture leverages INRs to learn a compressed latent representation of an entire time series dataset. The output of the HyperNet is a one-dimensional 7500-values embedding that contains the network weights of an INR (HypoNet) which encodes the time series data from the input.
Cloud Intelligence/AIOps – Infusing AI into Cloud Computing Systems (Microsoft)
AIOps is a rapidly emerging technology trend and an interdisciplinary research direction across system, software engineering, and AI/ML communities. With years of research on Cloud Intelligence, Microsoft Research has built up rich technology assets in detection, diagnosis, prediction, and optimization.
AIOps is a rapidly emerging technology trend and an interdisciplinary research direction across system, software engineering, and AI/ML communities. With years of research on Cloud Intelligence, Microsoft Research has built up rich technology assets in detection, diagnosis, prediction, and optimization.
Scientists and government representatives meeting at a conference in France have voted to scrap leap seconds by 2035, the organisation responsible for global timekeeping has said.
In November 2022 at the 27th General Conference on Weights and Measures, held about every four years at the Versailles Palace, it was decided to abandon the leap second by or before 2035. From then the difference between atomic and astronomical time will be allowed to grow to a larger value yet to be determined.
In November 2022 at the 27th General Conference on Weights and Measures, held about every four years at the Versailles Palace, it was decided to abandon the leap second by or before 2035. From then the difference between atomic and astronomical time will be allowed to grow to a larger value yet to be determined.
the Guardian
Do not adjust your clock: scientists call time on the leap second
Second added periodically to synchronise atomic time and Earth time can cause problems for GPS systems, software and telecoms
CS598: Machine Learning for Software Engineering
- Code representation and embeddings
- Source code analysis
- Code summarization
- Test input generation
- Fuzz testing
- Oracle inference
- Fault localization
- Program (bug) repair
- Regression testing
- Security testing and vulnerability detection
- Code completion
- Clone detection
- Code representation and embeddings
- Source code analysis
- Code summarization
- Test input generation
- Fuzz testing
- Oracle inference
- Fault localization
- Program (bug) repair
- Regression testing
- Security testing and vulnerability detection
- Code completion
- Clone detection
🔥2
Course: Machine Learning for Software Engineering (Ural State University)
- Introduction to machine learning
- Introduction to Transformer
- Code representation 1
- Code representation 2
- Code generation
- Code summarization
- Clone detection
- Code search 1
- Code search 2
- Code completion
- Vulnerabilities
- Introduction to machine learning
- Introduction to Transformer
- Code representation 1
- Code representation 2
- Code generation
- Code summarization
- Clone detection
- Code search 1
- Code search 2
- Code completion
- Vulnerabilities
GitHub
GitHub - konygin/course_ml4se: ML4SE course
ML4SE course. Contribute to konygin/course_ml4se development by creating an account on GitHub.
Large Language Models Can Self-Improve
CoT + multiple path decoding + self-consistency = effective self-training
74.4%->82.1% on GSM8K
78.2%->83.0% on DROP
90.0%->94.4% on OpenBookQA
63.4%->67.9% on ANLI-A3
CoT + multiple path decoding + self-consistency = effective self-training
74.4%->82.1% on GSM8K
78.2%->83.0% on DROP
90.0%->94.4% on OpenBookQA
63.4%->67.9% on ANLI-A3
Is effective self-training possible for small and medium-sized models?
Anonymous Poll
57%
Yes
43%
No
CodeQL code scanning launches Kotlin analysis support
Starting November 28, GitHub code scanning includes beta support for analyzing code written in Kotlin, powered by the CodeQL engine.
Starting November 28, GitHub code scanning includes beta support for analyzing code written in Kotlin, powered by the CodeQL engine.
Advent of Code is an annual set of Christmas-themed computer programming challenges that follow an Advent calendar. It has been running since 2015. The programming puzzles cover a variety of skill sets and skill levels and can be solved using any programming language.
OpenAI Solved Part 1 in 10 Seconds
https://www.reddit.com/r/adventofcode/comments/zb942v/2022_day_03_first_place_for_part_1_today_10/
OpenAI Solved Part 1 in 10 Seconds
https://www.reddit.com/r/adventofcode/comments/zb942v/2022_day_03_first_place_for_part_1_today_10/
Reddit
r/adventofcode on Reddit: [2022 Day 03] First place for part 1 today (10 seconds!) was fully automated using new OpenAI language…
Posted by u/rk-imn - No votes and 3 comments
ENASE 2023
Position/Regular Paper Submission: January 19, 2023
Doctoral Consortium Paper Submission: March 1, 2023
Abstracts Track Submission: March 1, 2023
Position/Regular Paper Submission: January 19, 2023
Doctoral Consortium Paper Submission: March 1, 2023
Abstracts Track Submission: March 1, 2023
enase.scitevents.org
ENASE, 21st Int'l. Conf. on Evaluation of Novel Approaches to Software Engineering
Theory and Practice of Systems and Applications Development, Challenges and Novel Approaches to Systems and Software Engineering (SSE), Systems and Software Quality, Systems and Software Engineering (SSE) for Emerging Domains
Ransomware Detection (Huawei)
* A baseline model is established based on historical data to check for any abnormalities in the changed feature values of the metadata of copies.
* Abnormal copies are further compared to determine file size changes, entropy values, and similarities.
* The Machine Learning (ML) model is used to determine whether file changes are caused by ransomware encryption, flagging them accordingly.
* A baseline model is established based on historical data to check for any abnormalities in the changed feature values of the metadata of copies.
* Abnormal copies are further compared to determine file size changes, entropy values, and similarities.
* The Machine Learning (ML) model is used to determine whether file changes are caused by ransomware encryption, flagging them accordingly.
Huawei BLOG
The Ransomware Story: Predicting the Unpredictable
Part 1 of a four-part series examining the features of ransomware, its devastating effects & the best way your company can stop it in its tracks.
DeepMind’s AlphaCode Conquers Coding, Performing as Well as Humans
DeepMind’s new coding AI just trounced roughly 50 percent of human coders in a highly competitive programming competition.
DeepMind’s new coding AI just trounced roughly 50 percent of human coders in a highly competitive programming competition.
Singularity Hub
DeepMind’s AlphaCode Conquers Coding, Performing as Well as Humans
AlphaCode paves the road for a novel way to design AI coders: forget past experience and just listen to the data.